Structural Multiplane Image: Bridging Neural View Synthesis and 3D Reconstruction Article Swipe
Mingfang Zhang
,
Jinglu Wang
,
Xiao Li
,
Yifei Huang
,
Yoichi Sato
,
Yan Lü
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2303.05937
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2303.05937
The Multiplane Image (MPI), containing a set of fronto-parallel RGBA layers, is an effective and efficient representation for view synthesis from sparse inputs. Yet, its fixed structure limits the performance, especially for surfaces imaged at oblique angles. We introduce the Structural MPI (S-MPI), where the plane structure approximates 3D scenes concisely. Conveying RGBA contexts with geometrically-faithful structures, the S-MPI directly bridges view synthesis and 3D reconstruction. It can not only overcome the critical limitations of MPI, i.e., discretization artifacts from sloped surfaces and abuse of redundant layers, and can also acquire planar 3D reconstruction. Despite the intuition and demand of applying S-MPI, great challenges are introduced, e.g., high-fidelity approximation for both RGBA layers and plane poses, multi-view consistency, non-planar regions modeling, and efficient rendering with intersected planes. Accordingly, we propose a transformer-based network based on a segmentation model. It predicts compact and expressive S-MPI layers with their corresponding masks, poses, and RGBA contexts. Non-planar regions are inclusively handled as a special case in our unified framework. Multi-view consistency is ensured by sharing global proxy embeddings, which encode plane-level features covering the complete 3D scenes with aligned coordinates. Intensive experiments show that our method outperforms both previous state-of-the-art MPI-based view synthesis methods and planar reconstruction methods.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2303.05937
- https://arxiv.org/pdf/2303.05937
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4324106139
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https://openalex.org/W4324106139Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2303.05937Digital Object Identifier
- Title
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Structural Multiplane Image: Bridging Neural View Synthesis and 3D ReconstructionWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-03-10Full publication date if available
- Authors
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Mingfang Zhang, Jinglu Wang, Xiao Li, Yifei Huang, Yoichi Sato, Yan LüList of authors in order
- Landing page
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https://arxiv.org/abs/2303.05937Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2303.05937Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2303.05937Direct OA link when available
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Computer science, Planar, Rendering (computer graphics), Polygon mesh, Artificial intelligence, Algorithm, Theoretical computer science, Computer graphics (images)Top concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.state-of-the-art | 196 |
| abstract_inverted_index.transformer-based | 131 |
| abstract_inverted_index.geometrically-faithful | 55 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.7099999785423279 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile |